The recent significant increase in the amount of music data being stored on both personal computers and Internet computers has created a need for ways to represent and classify music. Music classification is an important tool that enables music consumers to manage an increasing amount of music in a variety of ways, such as locating and retrieving music, indexing music, recommending music to others, archiving music, and so on. Various types of metadata are often associated with music as a way to represent music. Although traditional information such as the name of the artist or the title of the work remains important, these metadata tags have limited applicability in many music-related queries. More recently, music management has been aided by the use of more semantic metadata, such as music similarity, style and mood. Thus, the use of metadata as a means of managing music has become increasingly focused on the content of the music itself.
Music similarity is one important metadata that is useful for representing and classifying music. Music genres, such as classical, pop, or jazz, are examples of music similarities that are often used to classify music. However, such genre metadata is rarely provided by the music creator, and music classification based on this type of information generally requires the manual entry of the information or the detection of the information from the waveform of the music.
Music mood information is another important metadata that can be useful in representing and classifying music. Music mood describes the inherent emotional meaning of a piece of music. Like music similarity metadata, music mood metadata is rarely provided by the music creator, and classification of music based on the music mood requires that the mood metadata be manually entered, or that it be detected from the waveform of the music. Music mood detection, however, remains a challenging task which has not yet been addressed with significant effort in the past.
Accordingly, there is a need for improvements in the art of music classification, which includes a need for improving the detectability of certain music metadata from music, such as music mood.